{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["006:55"], "contactPoint": {"fn": "Kevin Garrity", "hasEmail": "mailto:kevin.garrity@nist.gov"}, "description": "The Interface materials design (InterMat) package introduces a multi-scale and data-driven approach for material interface/heterostructure design. This package allows:\n\n-Generation of an atomistic interface geometry given two similar or different materials,\n-Performing calculations using multi-scale methods such as DFT, MD/FF, ML, TB, QMC, TCAD etc.,\n-Analyzing properties such as equilibrium geometries, energetics, work functions, ionization potentials, electron affinities, band offsets, carrier effective masses, mobilities, and thermal conductivities, classification of heterojunctions, benchmarking calculated properties with experiments,\n-training machine learning models especially to accelerate interface design.", "distribution": [{"accessURL": "https://github.com/usnistgov/intermat", "description": "The Interface materials design (InterMat) package introduces a multi-scale and data-driven approach for material interface/heterostructure design.", "format": "github repo in python, linked to machine learning resources", "title": "Github of the intermat code on usnistgov"}, {"description": "NIST software checklist", "downloadURL": "https://data.nist.gov/od/ds/mds2-4023/KFG_final-g-1801.01-appendix-a-ver-1-fillable-form.pdf", "format": "pdf", "mediaType": "application/pdf", "title": "NIST software checklist"}], "identifier": "ark:/88434/mds2-4023", "issued": "2026-04-08", "keyword": ["Density functional theory", "defects", "force-field", "interfaces", "intermat", "machine learning", "semiconductors"], "landingPage": "https://data.nist.gov/od/id/mds2-4023", "language": ["en"], "license": "https://www.nist.gov/open/license", "modified": "2025-09-10 00:00:00", "programCode": ["006:045"], "publisher": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "references": ["https://pubs.rsc.org/en/content/articlelanding/2024/dd/d4dd00031e"], "theme": ["Chemistry:Theoretical chemistry and modeling", "Electronics:Semiconductors", "Materials:Modeling and computational material science", "Nanotechnology:Nanoelectronics", "Physics:Condensed matter"], "title": "InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning"}